Take-home Exercise 1: Application of Spatial Point Patterns Analysis to discover the geographical distribution of functional and non-function water points in Osub State, Nigeria

Published

January 30, 2023

1 Overview

1.1 Setting the Scene

Water is an important resource to mankind. Clean and accessible water is critical to human health. It provides a healthy environment, a sustainable economy, reduces poverty and ensures peace and security. Yet over 40% of the global population does not have access to sufficient clean water. By 2025, 1.8 billion people will be living in countries or regions with absolute water scarcity, according to UN-Water. The lack of water poses a major threat to several sectors, including food security. Agriculture uses about 70% of the world’s accessible freshwater.

Developing countries are most affected by water shortages and poor water quality. Up to 80% of illnesses in the developing world are linked to inadequate water and sanitation. Despite technological advancement, providing clean water to the rural community is still a major development issues in many countries globally, especially countries in the Africa continent.

To address the issue of providing clean and sustainable water supply to the rural community, a global Water Point Data Exchange (WPdx) project has been initiated. The main aim of this initiative is to collect water point related data from rural areas at the water point or small water scheme level and share the data via WPdx Data Repository, a cloud-based data library. What is so special of this project is that data are collected based on WPDx Data Standard.

1.2 Objectives

Geospatial analytics hold tremendous potential to address complex problems facing society. In this study, you are tasked to apply appropriate spatial point patterns analysis methods to discover the geographical distribution of functional and non-function water points and their co-locations if any in Osun State, Nigeria.

1.3 The Data

1.3.1 Aspatial Data

For the purpose of this assignment, data from WPdx Global Data Repositories will be used. There are two versions of the data. They are: WPdx-Basic and WPdx+. You are required to use WPdx+ data set.

1.3.2 Spatial Data

Nigeria Level-2 Administrative Boundary (also known as Local Government Area) polygon features GIS data will be used in this take-home exercise. The data can be downloaded either from The Humanitarian Data Exchange portal or geoBoundaries.

1.4 The Task

The specific tasks of this take-home exercise are as follows:

Exploratory Spatial Data Analysis (ESDA)

  • Derive kernel density maps of functional and non-functional water points. Using appropriate tmap functions,

  • Display the kernel density maps on openstreetmap of Osub State, Nigeria. Describe the spatial patterns revealed by the kernel density maps.

  • Highlight the advantage of kernel density map over point map.

Second-order Spatial Point Patterns Analysis

With reference to the spatial point patterns observed in ESDA:

  • Formulate the null hypothesis and alternative hypothesis and select the confidence level.

  • Perform the test by using appropriate Second order spatial point patterns analysis technique.

  • With reference to the analysis results, draw statistical conclusions.

Spatial Correlation Analysis

In this section, you are required to confirm statistically if the spatial distribution of functional and non-functional water points are independent from each other.

  • Formulate the null hypothesis and alternative hypothesis and select the confidence level.

  • Perform the test by using appropriate Second order spatial point patterns analysis technique.

  • With reference to the analysis results, draw statistical conclusions.

2 Getting Started

The R packages we’ll use for this analysis are:

  • sf: used for importing, managing, and processing geospatial data

  • tidyverse: a collection of packages for data science tasks

  • tmap: used for creating thematic maps, such as choropleth and bubble maps

  • spatstat: used for point pattern analysis

  • raster: reads, writes, manipulates, analyses and models gridded spatial data (e.g. raster-based geographical data)

  • funModeling: covers common aspects in predictive modeling (e.g. data cleaning, variable importance analysis and assessing model performance)

  • sfdep: performing geospatia data wrangling and local colocation quotient analysis

pacman::p_load(tmap, tidyverse, sf, funModeling, sfdep)

3 Handling Geospatial Data

3.1 Importing Geospatial Data

We will be importing the following geospatial datasets in R by using st_read() of sf package:

  • The geoBoundaries Dataset

  • The NGA data set

3.1.1 The geoBoundaries Dataset
geoNGA <- st_read("data/geospatial/",
                  layer = "geoBoundaries-NGA-ADM2") %>%
  st_transform(crs = 26392)
Reading layer `geoBoundaries-NGA-ADM2' from data source 
  `C:\michellefaithl\is415-gaa-michellefaith\Take-home_Ex\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 774 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84
3.1.2 The NGA Dataset
NGA <- st_read("data/geospatial/",
               layer = "geoBoundaries-NGA-ADM2") %>%
  st_transform(crs = 26392)
Reading layer `geoBoundaries-NGA-ADM2' from data source 
  `C:\michellefaithl\is415-gaa-michellefaith\Take-home_Ex\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 774 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84

By examining both sf dataframe closely, we notice that NGA provide both LGA and state information. Hence, NGA data.frame will be used for the subsequent processing.

3.2 Importing Aspatial Data

We will use read_csv() of readr package to import only water points within Nigeria.

wp_nga <- read_csv("data/aspatial/WPdx.csv") %>%
  filter(`#clean_country_name` == "Nigeria")
3.2.1 Converting water point data into sf point features

Converting an aspatial data into an sf data.frame involves two steps.

First, we need to convert the wkt field into sfc field by using st_as_sfc() data type.

wp_nga$Geometry = st_as_sfc(wp_nga$`New Georeferenced Column`)
wp_nga
# A tibble: 95,008 × 71
   row_id `#source`      #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
    <dbl> <chr>            <dbl>   <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
 1 429068 GRID3             7.98    5.12 08/29/… Unknown <NA>    <NA>    Tapsta…
 2 222071 Federal Minis…    6.96    3.60 08/16/… Yes     Boreho… Well    Mechan…
 3 160612 WaterAid          6.49    7.93 12/04/… Yes     Boreho… Well    Hand P…
 4 160669 WaterAid          6.73    7.65 12/04/… Yes     Boreho… Well    <NA>   
 5 160642 WaterAid          6.78    7.66 12/04/… Yes     Boreho… Well    Hand P…
 6 160628 WaterAid          6.96    7.78 12/04/… Yes     Boreho… Well    Hand P…
 7 160632 WaterAid          7.02    7.84 12/04/… Yes     Boreho… Well    Hand P…
 8 642747 Living Water …    7.33    8.98 10/03/… Yes     Boreho… Well    Mechan…
 9 642456 Living Water …    7.17    9.11 10/03/… Yes     Boreho… Well    Hand P…
10 641347 Living Water …    7.20    9.22 03/28/… Yes     Boreho… Well    Hand P…
# … with 94,998 more rows, 62 more variables: `#water_tech_category` <chr>,
#   `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
#   `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
#   `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
#   `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
#   `#pay` <chr>, `#fecal_coliform_presence` <chr>,
#   `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …

Next, we will convert the tibble data.frame into an sf object by using st_sf(). It is also important for us to include the referencing system of the data into the sf object.

wp_sf <- st_sf(wp_nga, crs=4326)
wp_sf
Simple feature collection with 95008 features and 70 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS:  WGS 84
# A tibble: 95,008 × 71
   row_id `#source`      #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
 *  <dbl> <chr>            <dbl>   <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
 1 429068 GRID3             7.98    5.12 08/29/… Unknown <NA>    <NA>    Tapsta…
 2 222071 Federal Minis…    6.96    3.60 08/16/… Yes     Boreho… Well    Mechan…
 3 160612 WaterAid          6.49    7.93 12/04/… Yes     Boreho… Well    Hand P…
 4 160669 WaterAid          6.73    7.65 12/04/… Yes     Boreho… Well    <NA>   
 5 160642 WaterAid          6.78    7.66 12/04/… Yes     Boreho… Well    Hand P…
 6 160628 WaterAid          6.96    7.78 12/04/… Yes     Boreho… Well    Hand P…
 7 160632 WaterAid          7.02    7.84 12/04/… Yes     Boreho… Well    Hand P…
 8 642747 Living Water …    7.33    8.98 10/03/… Yes     Boreho… Well    Mechan…
 9 642456 Living Water …    7.17    9.11 10/03/… Yes     Boreho… Well    Hand P…
10 641347 Living Water …    7.20    9.22 03/28/… Yes     Boreho… Well    Hand P…
# … with 94,998 more rows, 62 more variables: `#water_tech_category` <chr>,
#   `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
#   `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
#   `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
#   `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
#   `#pay` <chr>, `#fecal_coliform_presence` <chr>,
#   `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …
3.2.2 Transforming the Nigeria projected coordinate system

We will now transform the projection from wgs84 to an appropriate projected coordinate system of Nigeria.

wp_sf <- wp_sf %>%
  st_transform(crs = 26392)

4 Data Wrangling for Water Point Data

Exploratory Data Analysis (EDA) is a popular approach to gain initial understanding of the data. In the code chunk below, freq() of funModeling package is used to reveal the distribution of water point status visually.

freq(data = wp_sf,
     input = '#status_clean')

                     #status_clean frequency percentage cumulative_perc
1                       Functional     45883      48.29           48.29
2                   Non-Functional     29385      30.93           79.22
3                             <NA>     10656      11.22           90.44
4      Functional but needs repair      4579       4.82           95.26
5 Non-Functional due to dry season      2403       2.53           97.79
6        Functional but not in use      1686       1.77           99.56
7         Abandoned/Decommissioned       234       0.25           99.81
8                        Abandoned       175       0.18           99.99
9 Non functional due to dry season         7       0.01          100.00

Figure above shows that there are nine classes in the #status_clean fields.

Next, code chunk below will be used to perform the following data wrangling tasksP - rename() of dplyr package is used to rename the column from #status_clean to status_clean for easier handling in subsequent steps. - select() of dplyr is used to include status_clean in the output sf data.frame. - mutate() and replace_na() are used to recode all the NA values in status_clean into unknown.

wp_sf_nga <- wp_sf %>% 
  rename(status_clean = '#status_clean') %>%
  select(status_clean) %>%
  mutate(status_clean = replace_na(
    status_clean, "unknown"))

4.1 Extracting Water Point Data

Now we are ready to extract the water point data according to their status.

The code chunk below is used to extract functional water point.

wp_functional <- wp_sf_nga %>%
  filter(status_clean %in%
           c("Functional",
             "Functional but not in use",
             "Functional but needs repair"))

The code chunk below is used to extract nonfunctional water point.

wp_nonfunctional <- wp_sf_nga %>%
  filter(status_clean %in%
           c("Abandoned/Decommissioned",
             "Abandoned",
             "Non-Functional due to dry season",
             "Non-Functional",
             "Non functional due to dry season"))

The code chunk below is used to extract water point with unknown status.

wp_unknown <- wp_sf_nga %>%
  filter(status_clean == "unknown")

Next, the code chunk below is used to perform a quick EDA on the derived sf data.frames.

freq(data = wp_functional,
     input = 'status_clean')

                 status_clean frequency percentage cumulative_perc
1                  Functional     45883      87.99           87.99
2 Functional but needs repair      4579       8.78           96.77
3   Functional but not in use      1686       3.23          100.00
freq(data = wp_nonfunctional,
     input = 'status_clean')

                      status_clean frequency percentage cumulative_perc
1                   Non-Functional     29385      91.25           91.25
2 Non-Functional due to dry season      2403       7.46           98.71
3         Abandoned/Decommissioned       234       0.73           99.44
4                        Abandoned       175       0.54           99.98
5 Non functional due to dry season         7       0.02          100.00
freq(data = wp_unknown,
     input = 'status_clean')

  status_clean frequency percentage cumulative_perc
1      unknown     10656        100             100

4.2 Performing Point-in-Polygon Count

Next, we want to find out the number of total, functional, nonfunctional and unknown water points in each LGA. This is performed in the following code chunk. First, it identifies the functional water points in each LGA by using st_intersects() of sf package. Next, length() is used to calculate the number of functional water points that fall inside each LGA.

NGA_wp <- NGA %>% 
  mutate(`total_wp` = lengths(
    st_intersects(NGA, wp_sf_nga))) %>%
  mutate(`wp_functional` = lengths(
    st_intersects(NGA, wp_functional))) %>%
  mutate(`wp_nonfunctional` = lengths(
    st_intersects(NGA, wp_nonfunctional))) %>%
  mutate(`wp_unknown` = lengths(
    st_intersects(NGA, wp_unknown)))

Notice that four new derived fields have been added into NGA_wp sf data.frame.

4.3 Visualing attributes by using statistical graphs

In this code chunk below, appropriate functions of ggplot2 package is used to reveal the distribution of total water points by LGA in histogram.

ggplot(data = NGA_wp,
       aes(x = total_wp)) + 
  geom_histogram(bins=20,
                 color="black",
                 fill="light blue") +
  geom_vline(aes(xintercept=mean(
    total_wp, na.rm=T)),
             color="red", 
             linetype="dashed", 
             size=0.8) +
  ggtitle("Distribution of total water points by LGA") +
  xlab("No. of water points") +
  ylab("No. of\nLGAs") +
  theme(axis.title.y=element_text(angle = 0))

5 Exploratory Spatial Data Analysis (ESDA)

5.1 Basic Choropleth Mapping

5.2.1 Visualising Distribution of Functional Water Point by LGA
tmap_mode("view")
p1 <- tm_shape(NGA_wp) +
  tm_fill("wp_functional",
          n = 10,
          style = "equal",
          palette = "Blues") +
  tm_borders(lwd = 0.1,
             alpha = 1) +
  tm_layout(main.title = "Distribution of functional water point by LGAs",
            legend.outside = FALSE)
p1
5.2.2 Visualising Distribution of Functional Water Point by LGA
tmap_mode("view")
p2 <- tm_shape(NGA_wp) +
  tm_fill("wp_nonfunctional",
          n = 10,
          style = "equal",
          palette = "Blues") +
  tm_borders(lwd = 0.1,
             alpha = 1) +
  tm_layout(main.title = "Distribution of nonfunctional water point by LGAs",
            legend.outside = FALSE)
p2

5.2 Spatial Patterns from Kernel Density Maps

The advantage of kernel density map over point map: - better visualisation - summarises information of small zones compared to other zones

6 Second-order Spatial Point Patterns Analysis

6.1 Basic Choropleth Mapping